que se leu este artigo
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"tieneTextoCompleto" => true "paginas" => array:1 [ 0 => array:2 [ "paginaInicial" => "677" "paginaFinal" => "679" ] ] "autores" => array:1 [ 0 => array:4 [ "autoresLista" => "Luís Paiva, Sérgio Barra, Lino Gonçalves" "autores" => array:3 [ 0 => array:4 [ "nombre" => "Luís" "apellidos" => "Paiva" "email" => array:1 [ 0 => "luisvpaiva@gmail.com" ] "referencia" => array:3 [ 0 => array:2 [ "etiqueta" => "<span class="elsevierStyleSup">a</span>" "identificador" => "aff0005" ] 1 => array:2 [ "etiqueta" => "<span class="elsevierStyleSup">b</span>" "identificador" => "aff0010" ] 2 => array:2 [ "etiqueta" => "<span class="elsevierStyleSup">*</span>" "identificador" => "cor0005" ] ] ] 1 => array:3 [ "nombre" => "Sérgio" "apellidos" => "Barra" "referencia" => array:1 [ 0 => array:2 [ "etiqueta" => "<span class="elsevierStyleSup">c</span>" "identificador" => "aff0015" ] ] ] 2 => array:3 [ "nombre" => "Lino" "apellidos" => "Gonçalves" "referencia" => array:2 [ 0 => array:2 [ "etiqueta" => "<span class="elsevierStyleSup">a</span>" "identificador" => "aff0005" ] 1 => array:2 [ "etiqueta" => "<span class="elsevierStyleSup">b</span>" "identificador" => "aff0010" ] ] ] ] "afiliaciones" => array:3 [ 0 => array:3 [ "entidad" => "Hospital Geral, Centro Hospitalar e Universitário de Coimbra, Coimbra, Portugal" "etiqueta" => "a" "identificador" => "aff0005" ] 1 => array:3 [ "entidad" => "Universidade de Coimbra, Coimbra, Portugal" "etiqueta" => "b" "identificador" => "aff0010" ] 2 => array:3 [ "entidad" => "Cardiology Department, Papworth Hospital NHS Foundation Trust, Cambridge, UK" "etiqueta" => "c" "identificador" => "aff0015" ] ] "correspondencia" => array:1 [ 0 => array:3 [ "identificador" => "cor0005" "etiqueta" => "⁎" "correspondencia" => "Corresponding author." ] ] ] ] "titulosAlternativos" => array:1 [ "pt" => array:1 [ "titulo" => "Estratificação de risco nas síndromas coronárias agudas: como poderá o Grace ser destronado?" ] ] "resumenGrafico" => array:2 [ "original" => 0 "multimedia" => array:7 [ "identificador" => "fig0005" "etiqueta" => "Figure 1" "tipo" => "MULTIMEDIAFIGURA" "mostrarFloat" => true "mostrarDisplay" => false "figura" => array:1 [ 0 => array:4 [ "imagen" => "gr1.jpeg" "Alto" => 1645 "Ancho" => 1587 "Tamanyo" => 183499 ] ] "descripcion" => array:1 [ "en" => "<p id="spar0005" class="elsevierStyleSimplePara elsevierViewall">Receiver operating characteristic curve analysis for the prediction of in-hospital mortality using the ProACS and GRACE scores. AUC: area under the curve; CI: confidence interval; SE: standard error.</p>" ] ] ] "textoCompleto" => "<span class="elsevierStyleSections"><p id="par0005" class="elsevierStylePara elsevierViewall">We read with much interest the recently published paper by Timóteo et al.<a class="elsevierStyleCrossRef" href="#bib0045"><span class="elsevierStyleSup">1</span></a> This study makes an important contribution to the expanding field of risk stratification in myocardial infarction (MI), which is the cornerstone for patient management. The authors developed a simple risk model from a large cohort of real-world MI patients, comprising common and easily accessible clinical variables, constructing a high-performing in-hospital mortality risk score.</p><p id="par0010" class="elsevierStylePara elsevierViewall">Several risk scores have been developed to predict outcomes in patients with acute coronary syndromes. The GRACE score<a class="elsevierStyleCrossRef" href="#bib0050"><span class="elsevierStyleSup">2</span></a> is the most widely used and has been shown to outperform other previously developed mortality risk models.<a class="elsevierStyleCrossRef" href="#bib0055"><span class="elsevierStyleSup">3</span></a> The ACHTUNG-Rule,<a class="elsevierStyleCrossRef" href="#bib0060"><span class="elsevierStyleSup">4</span></a> based on clinical and laboratory parameters, outperformed the GRACE score in its derivation and validation sets but, in addition to requiring a calculator, it still lacks calibration and validation in larger and external cohorts. McNamara et al.<a class="elsevierStyleCrossRef" href="#bib0065"><span class="elsevierStyleSup">5</span></a> recently developed a novel in-hospital mortality risk score from a large contemporary cohort, with calibration curves demonstrating its utility even in patients presenting with cardiac arrest, a significant advantage in the context of MI given the difficulty in accurately predicting outcomes in this patient subgroup.</p><p id="par0015" class="elsevierStylePara elsevierViewall">Although the GRACE and ProACS<a class="elsevierStyleCrossRef" href="#bib0045"><span class="elsevierStyleSup">1</span></a> scores share some variables, the latter does have some advantages: (i) it is simpler to use in clinical practice, with fewer variables and no need for a calculator; (ii) it uses the same cutoffs for ST-elevation (STEMI) and non-ST-elevation (NSTEMI) MI, unlike GRACE; and (iii) it is particularly useful in identifying truly low-risk patients, as a score of 0 is associated with very low risk of in-hospital mortality (0.4%). In contrast, the GRACE score predicts mortality risk as a continuum, although low-risk strata have been proposed for STEMI and NSTEMI cases.</p><p id="par0020" class="elsevierStylePara elsevierViewall">We validated the ProACS risk model in an independent and somewhat more contemporary (2010-2013) cohort of 1000 consecutive MI patients (43.5% with STEMI). <a class="elsevierStyleCrossRef" href="#fig0005">Figure 1</a> illustrates the performance of both GRACE and ProACS for predicting in-hospital mortality in our cohort. Although the intermediate and high-risk strata of the ProACS score identified nearly 98% of in-hospital deaths, confirming the utility of the score in the stratification of ACS patients, its discriminative power was significantly inferior to that reported in its internal and external validation cohorts. Perhaps more importantly, the discriminative performance of ProACS was significantly inferior to that of GRACE.</p><elsevierMultimedia ident="fig0005"></elsevierMultimedia><p id="par0025" class="elsevierStylePara elsevierViewall">We believe that any new risk score should provide simplicity without significantly compromising accuracy of risk stratification. Using a more intuitive approach with a measure of risk reclassification, known as the integrated discrimination improvement (IDI) index,<a class="elsevierStyleCrossRef" href="#bib0070"><span class="elsevierStyleSup">6</span></a> we noticed that compared with GRACE, the ProACS score is indeed particularly useful in identifying truly low-risk patients who could potentially be considered for conservative management or early discharge (a 56.3% improvement in prediction for patients who did not die during hospitalization). However, this comes at the expense of a significantly lower (61.8%) predictive ability in the identification of patients who eventually die during their hospitalization. The relative IDI index was -5.5%. We further expanded on this by using a category-based measure of risk reclassification known as net reclassification improvement (NRI).<a class="elsevierStyleCrossRef" href="#bib0070"><span class="elsevierStyleSup">6</span></a> A positive and significant NRI index translates a net overall successful reclassification of subjects into more appropriate risk categories (e.g. a patient who reaches the primary endpoint who is reclassified into a higher risk group with the new model or a subject who does not reach the primary endpoint who is reclassified into a lower risk category). We used the same risk categories proposed by the GRACE and ProACS investigators. As seen in <a class="elsevierStyleCrossRefs" href="#tbl0005">Tables 1 and 2</a>, altogether the ProACS score reclassified 26.2% of STEMI patients and 17.7% of NSTEMI patients into risk strata which were less accurate representations of their observed mortality risks. However, ProACS did reclassify low-risk NSTEMI patients into more appropriate (lower) risk categories (partial NRI of +9.3%). The above observations suggest that the GRACE score is superior in identifying high-risk patients, whereas ProACS may be more useful in identifying low-risk patients who present with NSTEMI (but not STEMI).</p><elsevierMultimedia ident="tbl0005"></elsevierMultimedia><elsevierMultimedia ident="tbl0010"></elsevierMultimedia><p id="par0030" class="elsevierStylePara elsevierViewall">Furthermore, the ability to also estimate mortality during clinical follow-up and across different types of MI would surely improve the applicability of ProACS, since risk models differ significantly in their predictive performance when applied to MI that arises from a primary coronary event (type 1) vs. supply/demand mismatch (type 2).<a class="elsevierStyleCrossRef" href="#bib0080"><span class="elsevierStyleSup">7</span></a></p><p id="par0035" class="elsevierStylePara elsevierViewall">In summary, although the authors should be commended for their attempt to improve and simplify risk stratification of MI patients, it remains to be determined whether the simplicity of this new score, derived from a large Portuguese registry, is offset by its inferior prognostic power compared to the gold standard ACS risk score.</p><span id="sec0005" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0005">Conflicts of interest</span><p id="par0040" class="elsevierStylePara elsevierViewall">The authors have no conflicts of interest to declare.</p></span></span>" "textoCompletoSecciones" => array:1 [ "secciones" => array:2 [ 0 => array:2 [ "identificador" => "sec0005" "titulo" => "Conflicts of interest" ] 1 => array:1 [ "titulo" => "References" ] ] ] "pdfFichero" => "main.pdf" "tienePdf" => true "multimedia" => array:3 [ 0 => array:7 [ "identificador" => "fig0005" "etiqueta" => "Figure 1" "tipo" => "MULTIMEDIAFIGURA" "mostrarFloat" => true "mostrarDisplay" => false "figura" => array:1 [ 0 => array:4 [ "imagen" => "gr1.jpeg" "Alto" => 1645 "Ancho" => 1587 "Tamanyo" => 183499 ] ] "descripcion" => array:1 [ "en" => "<p id="spar0005" class="elsevierStyleSimplePara elsevierViewall">Receiver operating characteristic curve analysis for the prediction of in-hospital mortality using the ProACS and GRACE scores. AUC: area under the curve; CI: confidence interval; SE: standard error.</p>" ] ] 1 => array:8 [ "identificador" => "tbl0005" "etiqueta" => "Table 1" "tipo" => "MULTIMEDIATABLA" "mostrarFloat" => true "mostrarDisplay" => false "detalles" => array:1 [ 0 => array:3 [ "identificador" => "at1" "detalle" => "Table " "rol" => "short" ] ] "tabla" => array:2 [ "leyenda" => "<p id="spar0015" class="elsevierStyleSimplePara elsevierViewall">NRI: net reclassification improvement.</p><p id="spar0020" class="elsevierStyleSimplePara elsevierViewall">For patients with ST-elevation myocardial infarction (STEMI), a GRACE score below 126 was considered low risk, a score between 126 and 154 was assigned to the intermediate risk category and above 154 was assigned to the high risk group.</p>" "tablatextoimagen" => array:1 [ 0 => array:2 [ "tabla" => array:1 [ 0 => """ <table border="0" frame="\n \t\t\t\t\tvoid\n \t\t\t\t" class=""><thead title="thead"><tr title="table-row"><th class="td" title="table-head " colspan="3" align="center" valign="top" scope="col">In-hospital all-cause mortality</th><th class="td" title="table-head " colspan="3" align="center" valign="top" scope="col" style="border-bottom: 2px solid black">GRACE (STEMI)</th><th class="td" title="table-head " align="" valign="top" scope="col"> \t\t\t\t\t\t\n \t\t\t\t</th></tr><tr title="table-row"><th class="td" title="table-head " align="" valign="top" scope="col" style="border-bottom: 2px solid black"> \t\t\t\t\t\t\n \t\t\t\t</th><th class="td" title="table-head " align="" valign="top" scope="col" style="border-bottom: 2px solid black"> \t\t\t\t\t\t\n \t\t\t\t</th><th class="td" title="table-head " align="" valign="top" scope="col" style="border-bottom: 2px solid black"> \t\t\t\t\t\t\n \t\t\t\t</th><th class="td" title="table-head " align="left" valign="top" scope="col" style="border-bottom: 2px solid black">Low risk \t\t\t\t\t\t\n \t\t\t\t</th><th class="td" title="table-head " align="left" valign="top" scope="col" style="border-bottom: 2px solid black">Moderate risk \t\t\t\t\t\t\n \t\t\t\t</th><th class="td" title="table-head " align="left" valign="top" scope="col" style="border-bottom: 2px solid black">High risk \t\t\t\t\t\t\n \t\t\t\t</th><th class="td" title="table-head " align="" valign="top" scope="col" style="border-bottom: 2px solid black"> \t\t\t\t\t\t\n \t\t\t\t</th></tr></thead><tbody title="tbody"><tr title="table-row"><td class="td" title="table-entry " rowspan="3" align="left" valign="top">Events (n=48)</td><td class="td" title="table-entry " rowspan="6" align="left" valign="middle">ProACS</td><td class="td" title="table-entry " align="left" valign="top">Low risk \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">0 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">0 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">0 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " rowspan="6" align="left" valign="top">NRI: -26.5%<br>p<0.001</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top">Moderate risk \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">1 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">2 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">9 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top">High risk \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">0 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">0 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">36 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="table-entry " rowspan="3" align="left" valign="top">Non-Events (n=387)</td><td class="td" title="table-entry " align="left" valign="top">Low risk \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">4 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">1 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">0 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top">Moderate risk \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">81 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">86 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">56 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top">High risk \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">1 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">12 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">146 \t\t\t\t\t\t\n \t\t\t\t</td></tr></tbody></table> """ ] "imagenFichero" => array:1 [ 0 => "xTab1514244.png" ] ] ] ] "descripcion" => array:1 [ "en" => "<p id="spar0010" class="elsevierStyleSimplePara elsevierViewall">Net reclassification improvement for in-hospital all-cause mortality, comparing ProACS and GRACE scores for ST-elevation myocardial infarction.</p>" ] ] 2 => array:8 [ "identificador" => "tbl0010" "etiqueta" => "Table 2" "tipo" => "MULTIMEDIATABLA" "mostrarFloat" => true "mostrarDisplay" => false "detalles" => array:1 [ 0 => array:3 [ "identificador" => "at2" "detalle" => "Table " "rol" => "short" ] ] "tabla" => array:2 [ "leyenda" => "<p id="spar0030" class="elsevierStyleSimplePara elsevierViewall">NRI: net reclassification improvement.</p><p id="spar0035" class="elsevierStyleSimplePara elsevierViewall">For patients with non-ST-segment elevation myocardial infarction (NSTEMI), a GRACE score below 109 was considered low risk, while scores between 109 and 140 and above 140 were assigned to intermediate and high risk categories, respectively.</p>" "tablatextoimagen" => array:1 [ 0 => array:2 [ "tabla" => array:1 [ 0 => """ <table border="0" frame="\n \t\t\t\t\tvoid\n \t\t\t\t" class=""><thead title="thead"><tr title="table-row"><th class="td" title="table-head " rowspan="2" align="center" valign="top" scope="col" colspan="3" style="border-bottom: 2px solid black">In-hospital all-cause mortality</th><th class="td" title="table-head " colspan="3" align="center" valign="top" scope="col" style="border-bottom: 2px solid black">GRACE (NSTEMI)</th><th class="td" title="table-head " align="" valign="top" scope="col"> \t\t\t\t\t\t\n \t\t\t\t</th></tr><tr title="table-row"><th class="td" title="table-head " align="left" valign="top" scope="col" style="border-bottom: 2px solid black">Low risk \t\t\t\t\t\t\n \t\t\t\t</th><th class="td" title="table-head " align="left" valign="top" scope="col" style="border-bottom: 2px solid black">Moderate risk \t\t\t\t\t\t\n \t\t\t\t</th><th class="td" title="table-head " align="left" valign="top" scope="col" style="border-bottom: 2px solid black">High risk \t\t\t\t\t\t\n \t\t\t\t</th><th class="td" title="table-head " align="" valign="top" scope="col" style="border-bottom: 2px solid black"> \t\t\t\t\t\t\n \t\t\t\t</th></tr></thead><tbody title="tbody"><tr title="table-row"><td class="td" title="table-entry " rowspan="3" align="left" valign="top">Events (n=37)</td><td class="td" title="table-entry " rowspan="6" align="left" valign="middle">ProACS</td><td class="td" title="table-entry " align="left" valign="top">Low risk \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">0 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">0 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">2 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " rowspan="6" align="left" valign="top">NRI: -17.6%<br>p=0.051</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top">Moderate risk \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">0 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">1 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">8 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top">High risk \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">0 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">0 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">26 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td" title="table-entry " rowspan="3" align="left" valign="top">Non-Events (n=528)</td><td class="td" title="table-entry " align="left" valign="top">Low risk \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">20 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">18 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">12 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top">Moderate risk \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">55 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">82 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">101 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="table-entry ; entry_with_role_rowhead " align="left" valign="top">High risk \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">0 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">27 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="table-entry " align="char" valign="top">213 \t\t\t\t\t\t\n \t\t\t\t</td></tr></tbody></table> """ ] "imagenFichero" => array:1 [ 0 => "xTab1514243.png" ] ] ] ] "descripcion" => array:1 [ "en" => "<p id="spar0025" class="elsevierStyleSimplePara elsevierViewall">Net reclassification improvement for in-hospital all-cause mortality, comparing ProACS and GRACE scores for non-ST-elevation myocardial infarction.</p>" ] ] ] "bibliografia" => array:2 [ "titulo" => "References" "seccion" => array:1 [ 0 => array:2 [ "identificador" => "bibs0015" "bibliografiaReferencia" => array:7 [ 0 => array:3 [ "identificador" => "bib0045" "etiqueta" => "1" "referencia" => array:1 [ 0 => array:2 [ "contribucion" => array:1 [ 0 => array:2 [ "titulo" => "ProACS risk score: an early and simple score for risk stratification of patients with acute coronary syndromes" "autores" => array:1 [ 0 => array:2 [ "etal" => true "autores" => array:3 [ 0 => "A.T. 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2022 Maro | 35 | 42 | 77 |
2022 Fevereiro | 26 | 25 | 51 |
2022 Janeiro | 29 | 25 | 54 |
2021 Dezembro | 27 | 33 | 60 |
2021 Novembro | 25 | 27 | 52 |
2021 Outubro | 28 | 40 | 68 |
2021 Setembro | 22 | 27 | 49 |
2021 Agosto | 28 | 31 | 59 |
2021 Julho | 23 | 17 | 40 |
2021 Junho | 21 | 22 | 43 |
2021 Maio | 40 | 31 | 71 |
2021 Abril | 63 | 34 | 97 |
2021 Maro | 57 | 16 | 73 |
2021 Fevereiro | 64 | 23 | 87 |
2021 Janeiro | 43 | 14 | 57 |
2020 Dezembro | 25 | 14 | 39 |
2020 Novembro | 25 | 15 | 40 |
2020 Outubro | 20 | 13 | 33 |
2020 Setembro | 41 | 14 | 55 |
2020 Agosto | 20 | 10 | 30 |
2020 Julho | 19 | 12 | 31 |
2020 Junho | 34 | 10 | 44 |
2020 Maio | 25 | 2 | 27 |
2020 Abril | 29 | 13 | 42 |
2020 Maro | 31 | 14 | 45 |
2020 Fevereiro | 73 | 29 | 102 |
2020 Janeiro | 27 | 9 | 36 |
2019 Dezembro | 39 | 10 | 49 |
2019 Novembro | 28 | 12 | 40 |
2019 Outubro | 19 | 7 | 26 |
2019 Setembro | 22 | 8 | 30 |
2019 Agosto | 29 | 9 | 38 |
2019 Julho | 30 | 15 | 45 |
2019 Junho | 31 | 15 | 46 |
2019 Maio | 24 | 18 | 42 |
2019 Abril | 16 | 15 | 31 |
2019 Maro | 67 | 13 | 80 |
2019 Fevereiro | 66 | 12 | 78 |
2019 Janeiro | 87 | 3 | 90 |
2018 Dezembro | 72 | 15 | 87 |
2018 Novembro | 43 | 4 | 47 |
2018 Outubro | 47 | 16 | 63 |
2018 Setembro | 32 | 9 | 41 |
2018 Agosto | 27 | 14 | 41 |
2018 Julho | 8 | 4 | 12 |
2018 Junho | 29 | 9 | 38 |
2018 Maio | 21 | 7 | 28 |
2018 Abril | 27 | 8 | 35 |
2018 Maro | 47 | 18 | 65 |
2018 Fevereiro | 26 | 13 | 39 |
2018 Janeiro | 19 | 5 | 24 |
2017 Dezembro | 33 | 25 | 58 |
2017 Novembro | 52 | 18 | 70 |
2017 Outubro | 60 | 39 | 99 |
2017 Setembro | 56 | 25 | 81 |
2017 Agosto | 8 | 12 | 20 |